25 resultados para Computational analysis


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Models of the filtration phenomenon describe the mass balance in bed filtration in terms of particle removal mechanisms, and allow for the determination of global particle removal efficiencies. These models are defined in terms of the geometry and characteristic elements of granule collectors, particles and fluid, and also the composition of the balance of forces that act in the particle collector system. This work analyzes particles collection efficiency comparing downflow and upflow direct filtration, taking into account the contribution of the gravitational factor of the settling removal efficiency in future proposal of initial collection efficiency models for upflow filtration. A qualitative analysis is also made of the proposal for the collection efficiency models for particle removal in direct downflow and upflow filtration using a Computational Fluid Dynamics (CFD) tool. This analysis showed a strong influence of gravitational factor in initial collection efficiency (t = 0) of particles, as well as the reasons of their values to be smaller for upflow filtration in comparison with the downflow filtration.

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In order to understand the influence of alkyl side chains on the gas-phase reactivity of 1,4-naphthoquinone derivatives, some 2-hydroxy-1,4-naphthoquinone derivatives have been prepared and studied by electrospray ionization tandem mass spectrometry in combination with computational quantum chemistry calculations. Protonation and deprotonation sites were suggested on the basis of gas-phase basicity, proton affinity, gas-phase acidity (?Gacid), atomic charges and frontier orbital analyses. The nature of the intramolecular interaction as well as of the hydrogen bond in the systems was investigated by the atoms-in-molecules theory and the natural bond orbital analysis. The results were compared with data published for lapachol (2-hydroxy-3-(3-methyl-2-butenyl)-1,4-naphthoquinone). For the protonated molecules, water elimination was verified to occur at lower proportion when compared with side chain elimination, as evidenced in earlier studies on lapachol. The side chain at position C(3) was found to play important roles in the fragmentation mechanisms of these compounds. Copyright (c) 2012 John Wiley & Sons, Ltd.

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Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

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Abstract Background MicroRNAs (miRNAs) are small regulatory RNAs, some of which are conserved in diverse plant genomes. Therefore, computational identification and further experimental validation of miRNAs from non-model organisms is both feasible and instrumental for addressing miRNA-based gene regulation and evolution. Sugarcane (Saccharum spp.) is an important biofuel crop with publicly available expressed sequence tag and genomic survey sequence databases, but little is known about miRNAs and their targets in this highly polyploid species. Results In this study, we have computationally identified 19 distinct sugarcane miRNA precursors, of which several are highly similar with their sorghum homologs at both nucleotide and secondary structure levels. The accumulation pattern of mature miRNAs varies in organs/tissues from the commercial sugarcane hybrid as well as in its corresponding founder species S. officinarum and S. spontaneum. Using sugarcane MIR827 as a query, we found a novel MIR827 precursor in the sorghum genome. Based on our computational tool, a total of 46 potential targets were identified for the 19 sugarcane miRNAs. Several targets for highly conserved miRNAs are transcription factors that play important roles in plant development. Conversely, target genes of lineage-specific miRNAs seem to play roles in diverse physiological processes, such as SsCBP1. SsCBP1 was experimentally confirmed to be a target for the monocot-specific miR528. Our findings support the notion that the regulation of SsCBP1 by miR528 is shared at least within graminaceous monocots, and this miRNA-based post-transcriptional regulation evolved exclusively within the monocots lineage after the divergence from eudicots. Conclusions Using publicly available nucleotide databases, 19 sugarcane miRNA precursors and one new sorghum miRNA precursor were identified and classified into 14 families. Comparative analyses between sugarcane and sorghum suggest that these two species retain homologous miRNAs and targets in their genomes. Such conservation may help to clarify specific aspects of miRNA regulation and evolution in the polyploid sugarcane. Finally, our dataset provides a framework for future studies on sugarcane RNAi-dependent regulatory mechanisms.

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The development of cloud computing services is speeding up the rate in which the organizations outsource their computational services or sell their idle computational resources. Even though migrating to the cloud remains a tempting trend from a financial perspective, there are several other aspects that must be taken into account by companies before they decide to do so. One of the most important aspect refers to security: while some cloud computing security issues are inherited from the solutions adopted to create such services, many new security questions that are particular to these solutions also arise, including those related to how the services are organized and which kind of service/data can be placed in the cloud. Aiming to give a better understanding of this complex scenario, in this article we identify and classify the main security concerns and solutions in cloud computing, and propose a taxonomy of security in cloud computing, giving an overview of the current status of security in this emerging technology.

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This paper presents an alternative coupling strategy between the Boundary Element Method (BEM) and the Finite Element Method (FEM) in order to create a computational code for the analysis of geometrical nonlinear 2D frames coupled to layered soils. The soil is modeled via BEM, considering multiple inclusions and internal load lines, through an alternative formulation to eliminate traction variables on subregions interfaces. A total Lagrangean formulation based on positions is adopted for the consideration of the geometric nonlinear behavior of frame structures with exact kinematics. The numerical coupling is performed by an algebraic strategy that extracts and condenses the equivalent soil's stiffness matrix and contact forces to be introduced into the frame structures hessian matrix and internal force vector, respectively. The formulation covers the analysis of shallow foundation structures and piles in any direction. Furthermore, the piles can pass through different layers. Numerical examples are shown in order to illustrate and confirm the accuracy and applicability of the proposed technique.

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Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

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The reduction of friction and wear in systems presenting metal-to-metal contacts, as in several mechanical components, represents a traditional challenge in tribology. In this context, this work presents a computational study based on the linear Archard's wear law and finite element modeling (FEM), in order to analyze unlubricated sliding wear observed in typical pin on disc tests. Such modeling was developed using finite element software Abaqus® with 3-D deformable geometries and elastic–plastic material behavior for the contact surfaces. Archard's wear model was implemented into a FORTRAN user subroutine (UMESHMOTION) in order to describe sliding wear. Modeling of debris and oxide formation mechanisms was taken into account by the use of a global wear coefficient obtained from experimental measurements. Such implementation considers an incremental computation for surface wear based on the nodal displacements by means of adaptive mesh tools that rearrange local nodal positions. In this way, the worn track was obtained and new surface profile is integrated for mass loss assessments. This work also presents experimental pin on disc tests with AISI 4140 pins on rotating AISI H13 discs with normal loads of 10, 35, 70 and 140 N, which represent, respectively, mild, transition and severe wear regimes, at sliding speed of 0.1 m/s. Numerical and experimental results were compared in terms of wear rate and friction coefficient. Furthermore, in the numerical simulation the stress field distribution and changes in the surface profile across the worn track of the disc were analyzed. The applied numerical formulation has shown to be more appropriate to predict mild wear regime than severe regime, especially due to the shorter running-in period observed in lower loads that characterizes this kind of regime.

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Computational fluid dynamics, CFD, is becoming an essential tool in the prediction of the hydrodynamic efforts and flow characteristics of underwater vehicles for manoeuvring studies. However, when applied to the manoeuvrability of autonomous underwater vehicles, AUVs, most studies have focused on the de- termination of static coefficients without considering the effects of the vehicle control surface deflection. This paper analyses the hydrodynamic efforts generated on an AUV considering the combined effects of the control surface deflection and the angle of attack using CFD software based on the Reynolds-averaged Navier–Stokes formulations. The CFD simulations are also independently conducted for the AUV bare hull and control surface to better identify their individual and interference efforts and to validate the simulations by comparing the experimental results obtained in a towing tank. Several simulations of the bare hull case were conducted to select the k –ω SST turbulent model with the viscosity approach that best predicts its hydrodynamic efforts. Mesh sensitivity analyses were conducted for all simulations. For the flow around the control surfaces, the CFD results were analysed according to two different methodologies, standard and nonlinear. The nonlinear regression methodology provides better results than the standard methodology does for predicting the stall at the control surface. The flow simulations have shown that the occurrence of the control surface stall depends on a linear relationship between the angle of attack and the control surface deflection. This type of information can be used in designing the vehicle’s autopilot system.